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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access In vitro and in vivo biolasing of fluorescent proteins suspended in liquid microdroplet cavities(Royal Society of Chemistry (RSC), 2014) Jonas, Alexandr; Anand, Suman; McGloin, David; Department of Physics; Department of Chemistry; Bayraktar, Halil; Kiraz, Alper; Aas, Mehdi; Karadağ, Yasin; Manioğlu, Selen; Faculty Member; Faculty Member; PhD Student; Department of Physics; Department of Chemistry; College of Sciences; N/A; 22542; N/A; N/A; N/AFluorescent proteins are indispensable for selective, quantitative visualization of localization, dynamics, and interactions of key molecular constituents of live cells. Incorporation of fluorescent proteins into an optical cavity can lead to a significant increase in fluorescence signal levels due to stimulated emission and light amplification in the cavity, forming a laser with biological gain medium. Utilization of lasing emission from fluorescent biological molecules can then greatly enhance the performance of fluorescence-based biosensors benefiting from the high sensitivity of non-linear lasing processes to small perturbations in the cavity and the gain medium. Here we study optofluidic biolasers that exploit active liquid optical resonators formed by surface-supported aqueous microdroplets containing purified yellow fluorescent protein or a suspension of live E. coli bacterial cells expressing the fluorescent protein. We first demonstrate lasing in fluorescent protein solutions at concentrations as tow as 49 mu M. Subsequently, we show that a single fluorescent bacterial cell of micrometre size confined in a droplet-based cavity can serve as a laser gain medium. Aqueous droplet microcavities allow the maintenance of the bacterial cells under conditions compatible with unimpeded growth. Therefore, our results also suggest a direct route to microscopic sources of laser light with self-regenerating gain media.Publication Open Access 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients(Public Library of Science, 2019) Dinçer, Cansu; Kaya, Tuğba; Tunçbağ, Nurcan; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; 26605; 8745Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways, revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between each group and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.Publication Open Access A cartridge based sensor array platform for multiple coagulation measurements from plasma(Royal Society of Chemistry (RSC), 2015) Bulut, Serpil; Yaralioglu, G. G.; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Chemical and Biological Engineering; Çakmak, Onur; Ermek, Erhan; Kılınç, Necmettin; Barış, İbrahim; Kavaklı, İbrahim Halil; Ürey, Hakan; PhD Student; Other; Researcher; Teaching Faculty; Faculty Member; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Sciences; N/A; 109991; N/A; 111629; 40319; 8579This paper proposes a MEMS-based sensor array enabling multiple clot-time tests for plasma in one disposable microfluidic cartridge. The versatile LoC (Lab-on-Chip) platform technology is demonstrated here for real-time coagulation tests (activated Partial Thromboplastin Time (aPTT) and Prothrombin Time (PT)). The system has a reader unit and a disposable cartridge. The reader has no electrical connections to the cartridge. This enables simple and low-cost cartridge designs and avoids reliability problems associated with electrical connections. The cartridge consists of microfluidic channels and MEMS microcantilevers placed in each channel. The microcantilevers are made of electroplated nickel. They are actuated remotely using an external electro-coil and the read-out is also conducted remotely using a laser. The phase difference between the cantilever oscillation and the coil drive is monitored in real time. During coagulation, the viscosity of the blood plasma increases resulting in a change in the phase read-out. The proposed assay was tested on human and control plasma samples for PT and aPTT measurements. PT and aPTT measurements from control plasma samples are comparable with the manufacturer's datasheet and the commercial reference device. The measurement system has an overall 7.28% and 6.33% CV for PT and aPTT, respectively. For further implementation, the microfluidic channels of the cartridge were functionalized for PT and aPTT tests by drying specific reagents in each channel. Since simultaneous PT and aPTT measurements are needed in order to properly evaluate the coagulation system, one of the most prominent features of the proposed assay is enabling parallel measurement of different coagulation parameters. Additionally, the design of the cartridge and the read-out system as well as the obtained reproducible results with 10 mu l of the plasma samples suggest an opportunity for a possible point-of-care application.Publication Open Access Tipping the scale from disorder to alpha-helix: folding of amphiphilic peptides in the presence of macroscopic and molecular interfaces(Public Library of Science, 2015) Globisch, Christoph; Peter, Christine; N/A; Department of Mechanical Engineering; Dalgıçdır, Cahit; Sayar, Mehmet; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 109820Secondary amphiphilicity is inherent to the secondary structural elements of proteins. By forming energetically favorable contacts with each other these amphiphilic building blocks give rise to the formation of a tertiary structure. Small proteins and peptides, on the other hand, are usually too short to form multiple structural elements and cannot stabilize them internally. Therefore, these molecules are often found to be structurally ambiguous up to the point of a large degree of intrinsic disorder in solution. Consequently, their conformational preference is particularly susceptible to environmental conditions such as pH, salts, or presence of interfaces. In this study we use molecular dynamics simulations to analyze the conformational behavior of two synthetic peptides, LKKLLKLLKKLLKL (LK) and EAA LAEALAEALAE (EALA), with built-in secondary amphiphilicity upon forming an alpha-helix. We use these model peptides to systematically study their aggregation and the influence of macroscopic and molecular interfaces on their conformational preferences. We show that the peptides are neither random coils in bulk water nor fully formed alpha helices, but adopt multiple conformations and secondary structure elements with short lifetimes. These provide a basis for conformation-selection and population-shift upon environmental changes. Differences in these peptides' response to macroscopic and molecular interfaces (presented by an aggregation partner) can be linked to their inherent alpha-helical tendencies in bulk water. We find that the peptides' aggregation behavior is also strongly affected by presence or absence of an interface, and rather subtly depends on their surface charge and hydrophobicity.Publication Open Access The structural pathway of interleukin 1 (IL-1) initiated signaling reveals mechanisms of oncogenic mutations and SNPs in inflammation and cancer(Public Library of Science, 2014) Nussinov, Ruth; (TBD); Gürsoy, Attila; Özbabacan, Saliha Ece Acuner; Keskin, Özlem; Faculty Member; (TBD); The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; 8745; N/A; 26605Structural pathways are important because they provide insight into signaling mechanisms; help understand the mechanism of disease-related mutations; and help in drug discovery. While extremely useful, common pathway diagrams lacking structural data are unable to provide mechanistic insight to explain oncogenic mutations or SNPs. Here we focus on the construction of the IL-1 structural pathway and map oncogenic mutations and SNPs to complexes in this pathway. Our results indicate that computational modeling of protein-protein interactions on a large scale can provide accurate, structural atom-level detail of signaling pathways in the human cell and help delineate the mechanism through which a mutation leads to disease. We show that the mutations either thwart the interactions, activating the proteins even in their absence or stabilize them, leading to the same uncontrolled outcome. Computational mapping of mutations on the interface of the predicted complexes may constitute an effective strategy to explain the mechanisms of mutations- constitutive activation or deactivation.Publication Open Access Human cancer protein-protein interaction network: a structural perspective(Public Library of Science, 2009) Department of Computer Engineering; Department of Chemical and Biological Engineering; Kar, Gözde; Gürsoy, Attila; Keskin, Özlem; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; N/A; 8745; 26605Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.Publication Open Access A compressed sensing framework for efficient dissection of neural circuits(Nature Publishing Group (NPG), 2019) Lee, Jeffrey B.; Yonar, Abdullah; Hallacy, Timothy; Shen, Ching-Han; Milloz, Josselin; Srinivasan, Jagan; Ramanathan, Sharad; Department of Physics; Kocabaş, Aşkın; Department of Physics; College of Sciences; 227753A fundamental question in neuroscience is how neural networks generate behavior. The lack of genetic tools and unique promoters to functionally manipulate specific neuronal subtypes makes it challenging to determine the roles of individual subtypes in behavior. We describe a compressed sensing-based framework in combination with non-specific genetic tools to infer candidate neurons controlling behaviors with fewer measurements than previously thought possible. We tested this framework by inferring interneuron subtypes regulating the speed of locomotion of the nematode Caenorhabditis elegans. We developed a real-time stabilization microscope for accurate long-term, high-magnification imaging and targeted perturbation of neural activity in freely moving animals to validate our inferences. We show that a circuit of three interconnected interneuron subtypes, RMG, AVB and SIA control different aspects of locomotion speed as the animal navigates its environment. Our work suggests that compressed sensing approaches can be used to identify key nodes in complex biological networks.Publication Open Access Fast and interpretable genomic data analysis using multiple approximate kernel learning(Oxford University Press (OUP), 2022) Ak, Ciğdem; Department of Industrial Engineering; Gönen, Mehmet; Bektaş, Ayyüce Begüm; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; Graduate School of Sciences and Engineering; 237468; N/AMotivation: dataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interpretability started to fail with large sample sizes, owing to their lack of scalability. To overcome this problem, we proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model. Our method extracts significant and meaningful information from the genomic data while conjointly learning a model for out-of-sample prediction. It is scalable with increasing sample size by approximating instead of calculating distinct kernel matrices. Results: to test our computational framework, namely, Multiple Approximate Kernel Learning (MAKL), we demonstrated our experiments on three cancer datasets and showed that MAKL is capable to outperform the baseline algorithm while using only a small fraction of the input features. We also reported selection frequencies of approximated kernel matrices associated with feature subsets (i.e. gene sets/pathways), which helps to see their relevance for the given classification task. Our fast and interpretable MKL algorithm producing sparse solutions is promising for computational biology applications considering its scalability and highly correlated structure of genomic datasets, and it can be used to discover new biomarkers and new therapeutic guidelines.